Advance Your Career with the AI Engineer Associate Certificate

Take your AI skills to the next level with the AI Engineer Associate Certificate Course—a hands-on, intermediate-level program designed to help you build real-world expertise in machine learning, deep learning, and AI agent development. Whether you’re an aspiring AI engineer, a data science practitioner, or a developer seeking to upskill, this course gives you a solid foundation in advanced AI techniques and the most in-demand tools like TensorFlow and PyTorch.

We begin with Feature Engineering and Model Evaluation, where you’ll learn how to prepare data for machine learning, extract meaningful features, and evaluate model performance using metrics like precision, recall, F1 score, and ROC-AUC. These skills are essential for building accurate, reliable, and production-ready ML models. Next, we’ll cover Advanced Machine Learning Algorithms, where you’ll explore real-world implementations of decision trees, random forests, gradient boosting, XGBoost, and ensemble learning. You’ll understand when and how to apply each algorithm for different data types and problem spaces.

Then we dive into Neural Networks and Deep Learning Fundamentals, giving you a clear understanding of perceptrons, activation functions, backpropagation, and network architectures. This section lays the groundwork for building your own deep learning models from scratch. By the end of the course, you’ll be able to build and deploy advanced ML models, understand the math and code behind neural networks, and confidently use both TensorFlow and PyTorch, while also preparing for more specialized AI roles or certifications.

What you will learn:

• Perform advanced feature engineering for machine learning models
• Evaluate model performance using precision, recall, F1, and AUC
• Apply decision trees, random forests, and gradient boosting algorithms
• Understand deep learning concepts like activation and backpropagation
• Build neural networks from scratch using Python
• Train and deploy models using TensorFlow and Keras
• Use PyTorch to build, optimize, and evaluate deep learning models
• Understand the fundamentals of AI agents and their real-world applications

Course Content:

• Sections: 9 sections
• Lectures: 140 lectures
• Duration: 19h 9m total length

Requirements:

• Basic knowledge of Python programming, including functions, loops, and data structures
• Prior exposure to introductory data science or completion of a beginner-level AI course
• Familiarity with basic mathematics concepts such as algebra, functions, and vectors
• Some understanding of probability and statistics is helpful but not mandatory
• A computer (Windows, macOS, or Linux) with stable internet access
• Ability to install and work with tools like Jupyter Notebook, TensorFlow, and PyTorch (installation instructions are provided)
• Curiosity and motivation to explore intermediate-to-advanced AI concepts
• Willingness to engage in hands-on coding and practice-based learning

Who is it for?

• Aspiring AI Engineers ready to move beyond foundational concepts
• Software developers looking to transition into AI or enhance their machine learning capabilities
• Data analysts or junior data scientists aiming to specialize in AI-driven solutions
• Students in computer science or related fields who want hands-on experience with real-world ML frameworks
• Tech professionals seeking to build a portfolio of intermediate AI projects
• Product managers and tech leads who need a deeper understanding of how AI models are trained, evaluated, and deployed.

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

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